Continental-scale animal tracking reveals functional movement classes across marine taxa

Acoustic telemetry is a principle tool for observing aquatic animals, but coverage over large spatial scales remains a challenge. To resolve this, Australia has implemented the Integrated Marine Observing System’s Animal Tracking Facility which comprises a continental-scale hydrophone array and coordinated data repository. This national acoustic network connects localized projects, enabling simultaneous monitoring of multiple species over scales ranging from 100 s of meters to 1000 s of kilometers. There is a need to evaluate the utility of this national network in monitoring animal movement ecology, and to identify the spatial scales that the network effectively operates over. Cluster analyses assessed movements and residency of 2181 individuals from 92 species, and identified four functional movement classes apparent only through aggregating data across the entire national network. These functional movement classes described movement metrics of individuals rather than species, and highlighted the plasticity of movement patterns across and within populations and species. Network analyses assessed the utility and redundancy of each component of the national network, revealing multiple spatial scales of connectivity influenced by the geographic positioning of acoustic receivers. We demonstrate the significance of this nationally coordinated network of receivers to better reveal intra-specific differences in movement profiles and discuss implications for effective management.

[1]  Carter T. Butts,et al.  Tools for Social Network Analysis , 2014 .

[2]  Amy F. Smoothey,et al.  Conservation challenges of sharks with continental scale migrations , 2015, Front. Mar. Sci..

[3]  Patricio A. Vela,et al.  A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm , 2012, Expert Syst. Appl..

[4]  Steven J. Cooke,et al.  Ocean Tracking Network Canada: A Network Approach to Addressing Critical Issues in Fisheries and Resource Management with Implications for Ocean Governance , 2011 .

[5]  Eric R. Dougherty,et al.  Suite of simple metrics reveals common movement syndromes across vertebrate taxa , 2017, Movement ecology.

[6]  Anil K. Jain Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[7]  A. Kassambara,et al.  Extract and Visualize the Results of Multivariate Data Analyses [R package factoextra version 1.0.7] , 2020 .

[8]  K. M. Schaefer,et al.  Tracking apex marine predator movements in a dynamic ocean , 2011, Nature.

[9]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[10]  O. Ovaskainen,et al.  State-space models of individual animal movement. , 2008, Trends in ecology & evolution.

[11]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[12]  John E. Parks,et al.  Fish wars: Conflict and collaboration in fisheries management in Southeast Asia , 2007 .

[13]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[14]  J. Tweedley,et al.  Simple shade plots aid better long-term choices of data pre-treatment in multivariate assemblage studies , 2013, Journal of the Marine Biological Association of the United Kingdom.

[15]  Toby A. Patterson,et al.  Multi Year Observations Reveal Variability in Residence of a Tropical Demersal Fish, Lethrinus nebulosus: Implications for Spatial Management , 2014, PloS one.

[16]  Alistair J. Hobday,et al.  Automated acoustic tracking of aquatic animals: scales, design and deployment of listening station arrays , 2006 .

[17]  Alistair J. Hobday,et al.  Dynamic Ocean Management: Identifying the Critical Ingredients of Dynamic Approaches to Ocean Resource Management , 2015 .

[18]  J. Musick,et al.  Management of Sharks and Their Relatives (Elasmobranchii) , 2000 .

[19]  J. Kocik,et al.  Aquatic animal telemetry: A panoramic window into the underwater world , 2015, Science.

[20]  Steven J. Cooke,et al.  Addressing Challenges in the Application of Animal Movement Ecology to Aquatic Conservation and Management , 2017, Front. Mar. Sci..

[21]  M. Heupel,et al.  Habitat and space use of an abundant nearshore shark, Rhizoprionodon taylori , 2014 .

[22]  Matthew E. Watts,et al.  Integrating research using animal‐borne telemetry with the needs of conservation management , 2017 .

[23]  Steven J. Cooke,et al.  To share or not to share in the emerging era of big data: perspectives from fish telemetry researchers on data sharing , 2017 .

[24]  Edward J. Brooks,et al.  Developing a deeper understanding of animal movements and spatial dynamics through novel application of network analyses , 2012 .

[25]  Mario Espinoza,et al.  Contrasting movements and connectivity of reef-associated sharks using acoustic telemetry: implications for management. , 2015, Ecological applications : a publication of the Ecological Society of America.

[26]  J. Dolman Implications for management. , 1986, Professional nurse.

[27]  P. Mumby,et al.  Larval dispersal and movement patterns of coral reef fishes, and implications for marine reserve network design , 2015, Biological reviews of the Cambridge Philosophical Society.

[28]  Stuart Bearhop,et al.  The consequences of unidentifiable individuals for the analysis of an animal social network , 2015, Animal Behaviour.

[29]  Kim Holland,et al.  Key Questions in Marine Megafauna Movement Ecology. , 2016, Trends in ecology & evolution.

[30]  Roger Proctor,et al.  Australia’s continental-scale acoustic tracking database and its automated quality control process , 2018, Scientific Data.

[31]  Robert G. Harcourt,et al.  Optimising the design of large-scale acoustic telemetry curtains , 2017 .

[32]  M. Fuentes,et al.  Justifying the need for collaborative management of fisheries bycatch: A lesson from marine turtles in Australia , 2016 .